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Industrial robotic systems (IRS) are systems composed of industrial robots that automate industrial processes. They execute repetitive tasks with high accuracy, replacing or supporting dangerous jobs. Consequently, a low failure rate is crucial in IRS. However, to the best of our knowledge, there is a lack of automated software testing for industrial robots. In this paper, we describe a test strategy implementation to apply BDD to automate acceptance testing for IRS.

Machine Learning Operations (MLOps) is an approach to managing the entire lifecycle of a machine learning model. It has evolved over the last years and has started attracting many people in research and businesses in the industry. It supports the development of machine learning (ML) pipelines typical in the phases of data collection, data pre-processing, building datasets, model training, hyper-parameters refinement, testing, and deployment to production. This complex pipeline workflow is a tedious process of iterative experimentation. Moreover, cloud computing services provide advanced features for managing ML stages and deploying them efficiently to production. Specifically, serverless computing has been applied in different stages of the machine learning pipeline. However, to the best of our knowledge, it is missing to know the serverless suitability and benefits it can provide to the ML pipeline. In this paper, we provide a systematic mapping study of machine learning systems applied on serverless architecture that include 53 relevant studies. During this study, we focused on (1) exploring the evolution trend and the main venues; (2) determining the researchers’ focus and interest in using serverless on machine learning; (3) discussing solutions that serverless computing provides to machine learning. Our results show that serverless usage is growing, and several venues are interested in the topic. In addition, we found that the most widely used serverless provider is AWS Lambda, where the primary application was used in the deployment of the ML model. Additionally, several challenges were explored, such as reducing cost, resource scalability, and reducing latency. We also discuss the potential challenges of adopting ML on serverless, such as respecting service level agreement, the cold start problem, security, and privacy. Finally, our contribution provides foundations for future research and applications that involve machine learning in serverless computing.

Context : Searching and selecting relevant evidence is crucial to answer research questions from secondary studies in Software Engineering (SE). The activities of search and selection of studies are labour-intensive, time-consuming and demand automation support. Objective : Our goal is to identify and summarize the state-of-the-art on automation support for searching and selecting evidence for secondary studies in SE. Method: We performed a systematic mapping on existing automating support to search and select evidence for secondary studies in SE, expanding our investigation in a cross-domain study addressing advancements from the medical field. Results : Our results show that the SE field has a variety of tools and Text Classification (TC) approaches to automate the search and selection activities. However, medicine has more well-established tools with a larger adoption than SE. Cross-validation and experiment are the most adopted methods to assess TC approaches. Furthermore, recall and precision are the most adopted assessment metrics. Conclusion : Automated approaches for searching and selecting studies in SE have not been applied in practice by SE researchers. Integrated and easy-to-use automated approaches addressing consolidated TC techniques can bring relevant advantages on workload and time saving for SE researchers who conduct secondary studies.

Context : A tertiary study can be performed to identify related reviews on a topic of interest. However, the elaboration of an appropriate and effective search string to detect secondary studies is challenging for Software Engineering (SE) researchers. Objective: The main goal of this study is to propose a suitable search string to detect secondary studies in SE, addressing issues such as the quantity of applied terms, relevance, recall and precision. Method: We analyzed seven tertiary studies under two perspectives: (1) structure – strings’ terms to detect secondary studies; and (2) field: where searching – titles alone or abstracts alone or titles and abstracts together, among others. We validate our string by performing a twostep validation process. Firstly, we evaluated the capability to retrieve secondary studies over a set of 1537 secondary studies included in 24 tertiary studies in SE. Secondly, we evaluated the general capacity of retrieving secondary studies over an automated search using the Scopus digital library. Results: Our string was capable to retrieve an optimum value of over 90% of the included secondary studies (recall) with a high general precision of almost 60%. Conclusion: The suitable search string for finding secondary studies in SE contains the terms “systematic review”, “literature review”, “systematic mapping”, “mapping study” and “systematic map”.

Open Source Software (OSS) has been recognized by the software development community as an effective way to deliver software. Unlike traditional software development, OSS development is driven by collaboration among developers spread geographically and motivated by common goals and interests. Besides this fact, it is recognized by OSS community the need of understand OSS development process and its activities. Our goal is to investigate the state-of-art about OSS process through conducting a systematic literature review providing an overview of how the OSS community has been investigating OSS process over past years identifying and summarizing OSS process activities and their characteristics as well as translating OSS process in a macro process through BPMN notation. As a result, we systematically analysed 33 studies presenting an overview of the state-of-art of researches regarding OSS process, a generalized OSS development macro process represented by BPMN notation with a detailed description of each OSS process activity and roles in OSS environment. We conclude that OSS process can be in practice further investigated by researchers. In addition, the presented OSS process can be used as a guide for OSS projects and being adapted according to each OSS project reality. It provides insights to managers and developers who want to improve their development process even in OSS and traditional environments. Finally, recommendations for OSS community regarding OSS process activities are provided.